diff --git a/_bibliography/pint.bib b/_bibliography/pint.bib
index aa880aa3..534f6f4f 100644
--- a/_bibliography/pint.bib
+++ b/_bibliography/pint.bib
@@ -7698,6 +7698,20 @@ @article{PeterssonEtAl2025
 	year = {2025},
 }
 
+@article{StumpEtAl2025,
+	author = {Stump, Benjamin C. and Arndt, Daniel and Rolchigo, Matt and Reeve, Samuel Temple},
+	doi = {10.1016/j.commatsci.2025.113684},
+	issn = {0927-0256},
+	journal = {Computational Materials Science},
+	month = {March},
+	pages = {113684},
+	publisher = {Elsevier BV},
+	title = {Toucan: A performance portable, scalable implementation of the DECA algorithm},
+	url = {http://dx.doi.org/10.1016/j.commatsci.2025.113684},
+	volume = {251},
+	year = {2025},
+}
+
 @unpublished{WangEtAl2025,
 	abstract = {This paper analyzes the SParareal algorithm for stochastic differential equations (SDEs). Compared to the classical Parareal algorithm, the SParareal algorithm accelerates convergence by introducing stochastic perturbations, achieving linear convergence over unbounded time intervals. We first revisit the classical Parareal algorithm and stochastic Parareal algorithm. Then we investigate mean-square stability of the SParareal algorithm based on the stochastic $\theta$-method for SDEs, deriving linear error bounds under four sampling rules. Numerical experiments demonstrate the superiority of the SParareal algorithm in solving both linear and nonlinear SDEs, reducing the number of iterations required compared to the classical Parareal algorithm.},
 	author = {Huanxin Wang and Junhan Lyu and Zicheng Peng and Min Li},